Arbeitspapier
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
We study whether and when parameter-driven time-varying parameter models lead to forecasting gains over observation-driven models. We consider dynamic count, intensity, duration, volatility and copula models, including new specifications that have not been studied earlier in the literature. In an extensive Monte Carlo study, we find that observation-driven generalised autoregressive score (GAS) models have similar predictive accuracy to correctly specified parameter-driven models. In most cases, differences in mean squared errors are smaller than 1% and model confidence sets have low power when comparing these two alternatives. We also find that GAS models outperform many familiar observation-driven models in terms of forecasting accuracy. The results point to a class of observation-driven models with comparable forecasting ability to parameter-driven models, but lower computational complexity.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 12-020/4
- Klassifikation
-
Wirtschaft
Forecasting Models; Simulation Methods
Financial Econometrics
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Generalised autoregressive score model
Importance sampling
Model confidence set
Nonlinear state space model
Weibull-gamma mixture
Zeitreihenanalyse
Prognoseverfahren
Monte-Carlo-Methode
Zustandsraummodell
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Koopman, Siem Jan
Lucas, Andre
Scharth, Marcel
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2012
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Koopman, Siem Jan
- Lucas, Andre
- Scharth, Marcel
- Tinbergen Institute
Entstanden
- 2012
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